A Visual Learning based Robotic Grasping System

Deep learning has promoted the development of many areas in computer vision and robotics. However, most of the researches focus on an individual task. In this paper, we design a multi-task robot system based on ROS platform and YOLO network to complete the object detection, positioning, and grasping tasks. In terms of hardware, a heterogeneous computing platform is established to achieve high computing power while reducing energy consumption. In terms of software, an algorithm framework is designed for the multi-task robot system according to the characters the heterogeneous computing platform. Experimental results on real data show that the proposed robot system achieves promising object detection, positioning and grasping performance.